Business Intelligence generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer, and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information; content delivery infrastructure systems for delivery and management of reports and analytics; data warehousing systems for cleansing and consolidating information from disparate sources; and, data management systems, such as relational databases or On Line Analytic Processing (OLAP) systems used to collect, store, and manage raw data.
In many organizations data is stored in multiple formats that are not readily compatible, such as relational and OLAP data sources or combinations of different relational or different OLAP databases. Additionally, in many organizations it is desirable to insulate a user from the complexities of the underlying data source. Therefore, it is advantageous to be able to work with data using a semantic layer that provides terms and abstracted logic associated with the underlying data.
Semantic layers for relational databases are known in the art. It would be advantageous to enhance the architecture of known semantic layers to support abstractions of custom calculated dimensions and measures and to support the concept of hierarchies for dimensions. Likewise, it would be advantageous if a single query could be applied to a combination of data sources, such as relational data sources, OLAP data sources, and combinations thereof.